Measures of Ruleset Quality Capable to Represent Uncertain Validity

Authors:
Martin Holeňa
Affiliations:
Institute of Computer Science, Academy of Sciences of the Czech Republic, Praha 8, Czech Republic 18207
Venue:
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2007

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Abstract

The paper deals with quality measures of rules extracted from data, more precisely with measures of the whole extracted rulesets. Three particular approaches to extending ruleset quality measures from classification to general rulesets are discussed, and one of them, capable to represent uncertain validity of rulesets for objects, is elaborated in some detail. In particular, a generalization of ROC curves is proposed. The approach is illustrated on rulesets extracted with four important methods from the well-known iris data.